Artificial lift techniques are widely used to enhance production for reservoirs with formation pressure too low to provide enough energy to directly lift fluids to the surface. Among various artificial lift techniques in the industry (such as Gas Lift, Hydraulic Pumping Units, Electric Submersible Pump, Progressive Cavity Pump, Plunger Lifts and Rod Pump techniques), the Sucker Rod Pump technique is the most commonly used artificial lift method. For example, rod pump systems currently constitute approximately 59% of all Artificial Lift in North America and 71% in the rest of the world. Furthermore, about 80% of United States oil wells are considered to be marginal or stripper wells, which produce an average of ten barrels per day or less over a twelve month period and primarily are produced using rod pump systems. In the United States, rod pump systems are currently used on about 350,000 wells.
There are many types of failures for rod pump systems including tubing failures, rod string failures and rod pump failures. The reasons for rod pump system failures can be broadly classified into two main categories: mechanical and chemical. Mechanical failures are caused by improper design, by improper manufacturing, or by wear and tear during operations. Well conditions such as sand intrusions, gas pounding, and asphalting can contribute to excessive wear and tear. Chemical failures are generally caused by the corrosive nature of the fluid being pumped through the systems. For example, the fluid may contain hydrogen sulfide (H2S) or bacteria that excrete corrosive chemicals. Mechanical and chemical failures initially reduce the efficiency of pumping operations, but in due course will bring the systems to fail, thus requiring reactive well work. Wells are shut down to perform workovers, which results in production loss and an increase in the operating expenditure (OPEX) in addition to the regular maintenance cost.
Currently, pump off controllers (POCs) play a significant role in monitoring and controlling the operation of rod pump systems. For example, the POCs can be programmed to automatically shut down units if the values of torque and load deviate beyond a torque/load threshold. While POCs reduce the amount of work required by the production and maintenance personnel operating the field, they may not be sufficient since a great deal of time and effort is still needed to monitor each and every operating unit. The dynamometer card patterns collected by the POCs can be analyzed to better understand the behavior of the rod pump systems. However, successful analysis is directly linked to the skill and experience of the analyst and even the most knowledgable analysts can be misled into an incorrect diagnosis. In some cases, the dynamometer card may miss some early warnings of rod pump system failures. Furthermore, the well measurement dataset obtained by POCs often poses difficult challenges to data mining with respect to high dimensionality, noise, and inadequate labeling.
The data collected from POCs is inherently highly dimensional as POC controllers gather and record periodic well sensor measurements indicating production and well status through load cells, motor sensors, pressure transducers and relays. For example, in a dataset having 14 attributes where each attribute is measured daily, the dimension is 1400 for a dataset over a hundred day period.
Datasets for well measurement artificial lift also tend to be very noisy. The noise is produced from multiple sources, which include natural and manmade causes. The wells operate in rough physical environments which often results in equipment break down. For example, lightning strikes can sometimes disrupt wireless communication networks. Data collected by the POC sensors is therefore not received by a centralized logging database, which results in missing values in the data. Additionally, petroleum engineering field workers regularly perform maintenance and make calibration adjustments to the equipment. These maintenance activities and adjustments can cause the sensor measurements to change—sometimes considerably. For example, the POC sensors are occasionally recalibrated, which can introduce extreme changes in sensor readings. It is not standard practice to record such recalibrations. Furthermore, while workers are generally diligent with regards to logging their work in downtime and workover database tables, occasionally a log entry is delayed or not logged at all. Another source of data noise is the variation caused by the force drive mechanisms. In oil fields with insufficient formation pressure, injection wells can be used to inject fluid (e.g., water, steam, polymer, carbon dioxide) into the reservoir to drive hydrocarbons toward production wells. This fluid injection can also affect the POC sensors measurements.
The datasets received by POCs are also not explicitly labeled. Manually labeling the dataset received by a POC is generally too time consuming and very tedious. Furthermore, access to petroleum engineering subject matter experts (SMEs) to perform the manual labeling is also often limited. Fully automatic labeling is also problematic. Although the well failure events are recorded in the well database, they are not suitable for direct use because of semantic differences in the interpretation of well failure dates. In general, the well failure dates in the database do not correspond to the actual failure dates, or even to the dates when the SMEs first notice the failures. Rather, the recorded failure dates typically correspond to the date when the workers shut down the well to begin repairs. Because of the backlog of well repair jobs, the difference can be several months between the actual failure dates and the recorded failure dates. Moreover, even if the exact failure dates are known, differentiation of the failures among normal, pre-failure and failure signals still needs to be performed.
FIG. 1 shows an example of a past well failure where several selected attributes collected through a POC are displayed. As shown in FIG. 1, the well's failure was detected by field personnel on Mar. 31, 2010. After pulling all the pumping systems above the ground, they discovered that there were holes on the tubing that were causing the leaking problems, which in turn, reduced the fluid load the rod pump carried to the surface. Through a “look back” process, it was found that the actual leak started around Feb. 24, 2010. Even before that, a subject matter expert established that “rod cut” events likely started around Nov. 25, 2009, wherein the rod began cutting the tubing. After the initial cutting, the problem continually grew worse cutting larger holes into the tubing.
The inventors therefore have recognized a need for more automated systems, such as artificial intelligent systems that can dynamically keep track of certain parameters in a group of artificial lift systems, detect impending system failures and provide early indications or warnings thereof, and provide suggestions on types of maintenance work to address the detected failures including providing an optimal work schedule for performing such work. Such systems would be a great asset to industry personnel by potentially allowing them to be more proactive and to make better maintenance decisions. These systems could increase the efficiency of the artificial lift systems to bring down Operating Expenditure (OPEX), thereby making the artificial lift operations more economical.